Furthermore, some works exploit pseudo labels for target domain data [1]}, [2]}, [3]}, and take them as labeled data to refine the model. Their success partly depends on the quality of the pseudo-labels obtained during the preprocessing step. Through the marginal sampling algorithm, [4]} collected and labeled a small number of representative samples of the target domain, and explicitly further optimizes and generalizes the decision boundary to the target distribution. However, professionals are required to label the samples multiple times during training.
